For hand-written deep parsers, efficiency and coverage are often competing factors: allowing more items to be created might be beneficial for recall, but the parser will also be too slow.

the authors show that a large, hand-written, unification-based parser (the XLE LFG parser for English) can perform reasonably faster (18%) without losing accuracy, by not allowing the parser to unify if the resulting item will have a span that does not conform to a CFG tree that was generated from the sentence beforehand by a PCFG parser.


The main system (henceforth, direct system), corresponding to configuration 1, is a SMT system, translating from German to English in the example.

we focussed on three translation tasks: one for which translation accuracy, as measured by automatic metrics, is rather high (f r → en), and two for which translation accuracy is lower (de → en) and (f r → de).


As can be seen, the (f r → de) system is still improved by using the additional language model.

auxiliary translations obtained by pivoting can be viewed as a source of adaptation data for the target language model of the direct system.


The mostly negative results on the translation of pronouns were expected, because this depends on their antecedent in English and is not the object of specific modelling from the systems.

the absolute value of the gain under the full condition (+0.61) is lower than that of the intersection data condition (+0.96).


In case where there is no such natural restrictions, we attempt to detect and add to our queries a few language-specific frequent words.

we do this by utilizing the strategy described in Section 3.1 on the obtained target language pattern clusters.


This makes it difficult to perform meaningful comparison to existing MT frameworks.

we also perform pattern-set to pattern-set translation rather than the pattern-to-pattern or pair-topair translation commonly explored in MT studies.


The vast majority of NLP research is done in a few languages for which extensive corpora (including the web) are available.

we evaluate our method on 11 diverse target languages.


The pair is then labeled with the label outputted by the classifier (i.e., the label receiving the highest probability).

the institution still went bankrupt e 4 a year later.


(2008) built a cross document coreference system using features from encyclopedic sources like Wikipedia.

we just recently became aware of a system fielded by Li et al.


The third one (Dep) is a string-to-dependency hierarchical phrase-based system (Shen et al., 2008) with a dependency language model, which translates source strings to target dependency trees.

bayesian Model Averaging is used to integrate values of identical features between models.


Similar to the trend in Table 2, we also notice that TMG method is more preferred by string-based systems (Phr and Hier) rather than tree-based systems (Dep and Synx).

due to different model structures or data distributions, these features are usually assigned with different values in different translation models and result in translation outputs with individual advantages and shortcomings.


Compared to MAP decoding, the best result (30.1) is obtained when the size is 100, and the largest improvement (+1.4 BLEU) is obtained when the size is 50.

although this training strategy can be processed repeatedly, the performance is stable after the 1 st round finished.


(2008) to use log-BLEU, which is scored by the hypothesis length and a linear function of n-gram matches as: In this definition, is a reference translation, is the length of hypothesis , is an ngram presented in , is the number of times that occurs in , and is an indicator function which equals to 1 when occurs in and 0 otherwise.

mBR-IHmm Word-Comb has ability to generate new hypotheses, while Hypergraph mmmBR only chooses translations from original search spaces.


For both grammaticality and informativity, for every summary-cluster pair we did majority voting and resolved ties by assigning the lower score.

this distinguishes our work from earlier research on sentence fusion and compression which relies on syntactic representations and/or language models.


The path with the minimum total weight is selected as the summary.

the raters were asked whether the cluster contained a single prevailing event, or whether it was too noisy and no theme stood out.


However, longer paths between words are weak signals of word association.

word graphs have been widely used in natural language processing for building language models, paraphrasing, alignment, etc.


OCAL benefits from being in the public domain; however, it cannot cover a wide variety of meanings because of the limited number of available images.

the image (3) may not be typical but is helpful for understanding, and (2) may lead to a misunderstanding if this is the only image shown to the dictionary user.


We show that our method is simple but effective.

table 3 shows the ratio of t (appropriate), M (acceptable) and F (inappropriate) images for the target sense.


As shown in Table 7, 90.5 % of "Concrete" nouns are judged as "Shown", and only 9.5 % of senses are judged as "Not Shown" 11 .

for information retrieval, query expansion using synonyms has been adopted in several studies (Voorhees, 1994;fang and Zhai, 2006;Unno et al., 2008).


Additionally, we use only normal spellings with no expansion, when the target words are monosemous (MONO).

with MC, both LNK and SYN have less precision.


As we mentioned in Section 1, the discriminative aligner is not restricted to use features parameters of generative models and Viterbi alignments.

as we mentioned in Section 1, the discriminative aligner is not restricted to use features parameters of generative models and Viterbi alignments.


Constraints (0, j) or (i, 0) are used to explicitly represent that word f j or e i is aligned to the empty word.

including the features from generative models is required for iterative training, because the improvement on the quality of these features can in turn improve the discriminative aligner.


In Section 2, we present the EMDC training scheme.

the discriminative training in these methods is restricted in using the model components of generative models, in other words, incorporating new features is difficult.


and then clicks on the resulting spelling suggestion "harry potter theme park".

we use this generative story: first the correctly spelled query C is broken into K non-empty word sequences c 1 , …, c k , then each is replaced with a new non-empty word sequence q 1 , …, q k , finally these phrases are permuted and concatenated to form the misspelled Q.


The grammar (SEMXTAG) used by GENI and RTGEN is a Feature-Based Lexicalised Tree Adjoining Grammar (FTAG) augmented with a unification-based semantics as described in (Gardent and Kallmeyer, 2003).

third, the retrieval phase extracts the yields of the complete derived trees, thereby producing the generated sentence(s).


For instance, the semantic index s in the tree for runs is unified during substitution with the semantic index labelling the root node of the tree for John.

one difference however it that RTGEN will pack analyses together as long as the new ones are more specific cases.


For every internal node in the parse tree, traversed top-down, the node is matched against the conditioning context, and if a match is found, the associated action applies.

we pretend to consider two nodes, one with the first set of children, and one with the second, proceeding left to right.


6 But to its great advantage, it has a high ratio of non-terminals/terminals (μ Constituents / μ Length).

cohen and Smith (2007) chose a metric like SParseval (Roark et al., 2006) that first aligns the trees and then penalizes segmentation errors with an edit-distance metric.


The sum (10-13) of the IDN, VHS, HS and MS document pairs found by the best full and selective fingerprint methods mentioned in the last paragraph is the same sum of the IDN, VHS, HS and MS document pairs found by the CA and CR methods.

usually, selfplagiarism is considered to be a serious ethical problem in cases where a publication needs to contain an important portion of a new material, such as in academic papers (Wikipedia, 2010).


When a complicated reordering model is used, our model can limit it in a smaller scope and replace it with an easier reordering model in larger scope.

a hierarchical phrase-based translation model (HPTM) reorganizes phrases into hierarchical ones by reducing sub-phrases to variables (Chiang 2005).


These models are phrase-dependent, so improvements over content-independent reordering models are obtained.

the generated translations of phrases in the higher-order layer are fed as a new added translation source into the next lower-order reordering model.


We will use categories with vertical slashes when the direction of the slash does not matter, and may omit unnecessary parentheses (so X|Y|Z will represent (X\Y)/Z, (X\Y)\Z, ...).

the derivations have different semantics.


Argument Y, head (X\Y)/Z and argument Z combine to X In the NF derivation, the head applies first to the Z, than to Y.

it and its equivalent reformulation by Hoyt and Baldridge (2008) in a multimodal variant of CCG are not safe (preserve all interpretations) and complete (remove all spurious ambiguities) for more commonly used variants of CCG.


These approaches (Zhao and Vogel, 2002;Utiyama and Isahara 2003;Fung and Cheung, 2004;Munteanu and Marcu, 2005;Abdul-Rauf and Schwenk, 2009) share roughly the same framework: Phase 1: Document Pair Retrieval 1) documents in some target language (TL) are stored in some database; 2) each document in some source language (SL) is represented by some TL keywords; 3) the TL keywords in (2) are used to assign some TL documents to a particular SL document, using some information retrieval (IR) technique.

the entire Web) without search constraint.


the learning algorithm used is Ranking SVM (Herbrich et al., 2000;Joachims, 2006), which is a state-of-the-art method of the "Learning to rank" framework.


In fact, they do not only satisfy the requirement of length and cue phrases (described in section 2), but they also have another property that they are translated from some English news articles (henceforth target pages) on the Web.

conventional approaches to comparable corpus mining usually start with document pair retrieval, which assigns to each SL document a set of candidate TL documents.


On the contrary, it seems that the second problem can somewhat be alleviated, if we have a way to distinguish those terms that are likely to be correct translations from those terms that are not.

this implies that bilingual news mining must be incrementally carried out.


The trained models can then be applied to rerank the keywords of simple queries.

they are ideal data for studying the performance of document pair retrieval.


In particular, TC3 is more significant than TC1 for reducing the Gini index because it recalls more correct categories.

the more recent Web Person Search (WePS) task (Artiles et al., 2007) has created a benchmark dataset which is also used in this work.


One of Figure 3: Importance of the expert components and their features found by Random Forest (note the small spread in MeanDecreaseAccuracy).

inverse purity improves by 46% relatively, implying that the additional evidence significantly improves the recall of coreferent entities (when there is a lack of context similarity in the traditional method).


Overall, our model can achieve more than 85% accuracy for pairwise coreference prediction.

affinity is the complementary of the normalized path length between c and its ancestor c .


Precision is computed by asking two experienced human experts to classify each suggestion of a given seed, as relevant or irrelevant.

for each extracted entity, we assign two confidence scores: a Web-based representation score and a query-log-based standalone score.


However, since the boundaries of the spans annotated in the MPQA corpus are not strictly defined in the annotation guidelines (Wiebe et al., 2005), measuring precision and recall using exact boundary scoring will result in figures that are too low to be indicative of the usefulness of the system.

we used the function ρ(y,ŷ) = 1 − F (y,ŷ), where F is the soft F-measure described in Section 4.1.


To handle this issue, we created positive training instances for all members of the coreference chain in the same sentence as the opinion, and negative instances for the other noun phrases.

we can improve on this by adding the holder interaction features: both the SHARED HOLDERS and HOLDER TYPES + PATH features contribute to improving the recall even further.


The trees generated by this grammar are sufficiently expressive to represent any possible segmentation of any sequence of phonemes into words (including the true segmentation); a typical segmentation is shown in Figure 1.

(In this and all of the other grammars in this paper, the start symbol is the non-terminal symbol of the first rule in the grammar.


The parser can therefore end up in a local maximum that is not the global maximum we are searching for.

a discussion of this issue is presented in Maier (2010).


A probabilistic LCFRS (PLCFRS) (Kato et al., 2006) is a tuple N, T, V, P, S, p such that N, T, V, P, S is a LCFRS and p : P → [0..1] a function such that for all A ∈ N : We use a probabilistic version of the CYK parser from (Seki et al., 1991), applying techniques of weighted deductive parsing (Nederhof, 2003).

our outside weights are only figures of merit (FOM).


Given a document D consisting of tokenized word sequence {w i : i = 1, 2, • • • , |D|}, its perplexity L(D|M j ) with respect to a LM M j is computed as: where |D| is the number of words in D and h i are the history words for w i , and P (w i |h i ; M j ) is the probability M j assigns to w i , when it follows the history words h i .

we used decision tree (reduced error pruning (Quinlan, 1987)) regression, decision tree regression with bagging (Breiman, 1996), support vector regression (Smola and Scholkopf, 1998) using polynomial kernel of degree two, 2 linear regression and Gaussian process regression (Rasmussen and williams, 2006).


Many times, a document is found to be unreadable due to unusual linguistic constructs or ungram-matical language that tend to manifest themselves in the syntactic properties of the text.

used stratified 13-fold cross-validation in which the documents from various genres in each fold was distributed in roughly the same proportion as in the overall dataset.


Table 4 shows the results comparing these two feature sets.

we compute the OOV rates of a document with respect to the various LMs shown in Table 1.


The second factor is the cost of sense annotated data from the target language.

in the absence of skilled bilingual lexicographers one can use a probabilistic cross-linking model and still obtain good accuracies.


The first factor is the cost of manually cross-linking the words in a synsets of the target language to the words in the corresponding synset in the pivot language.

some similar work has been done in the area of domain adaptation where Chan et al.


As mentioned earlier, in some cases where bilingual lexicographers are expensive we might be interested in reducing the effort of manual crosslinking.

this strategy of random sampling may not be the most optimum in terms of cost.


In PG1, edges D and E are alternatives corresponding to the different prepositional phrase attachments for "with its spark plug" (to ignite-3a or gasoline-4a).


This change will result in a significant increase in the number of alternatives stored in the PG representation over the current prototype.

the baseline has pruned away 24% of the correct types and 26% of the correct semantic relations.


A possible explanation for this phenomenon is that people create links between related documents that provide new information and therefore do not link nearly identical content.

more specifically, can the value of semantic similarity be used as a predictor for relationship typing?


Experiments on the ACE 2003 corpus showed that this joint anaphoricity-coreference ILP formulation improved the F1-measure by 3.7-5.3 on various domains.

for preparation, all the documents in the corpus are preprocessed automatically using a pipeline of NLP components, including tokenization and sentence segmentation, named entity recognition, part-of-speech tagging and noun phrase chunking.


Only 2-4% of the total phrases in each language end up in a cluster (that's 6.5-9% of eligible phrases, i.e., of phrases that aren't "count 1").

distance between clusters 1 and 2 would be Edit("red", "dark burgundy")=1.0.


In Experiment 4, using 1,350 frequent terms, we examine the effects of seed lexicon size and specificity and we apply a heuristic based on cognates.

according to our results, translation spotting is more accurate when the seed lexicon contains (5,000) entries from both the medical domain and general language instead of general language words only, but only by a very small margin.


Some reordering rules cannot be captured by CFG rules, and we resort to CSG rules.

vBX stands for vB, vBZ, vBP, vBD, vBN and vBG.


Polarity shifting plays a crucial role in phrase-level, sentence-level, and document-level sentiment classification.

in the literature, machine learning approaches have dominated the research in sentiment classification and achieved the state-of-the-art performance (e.g., Kennedy and inkpen, 2006;Pang et al., 2002).


This enables our approach to handle different kinds of polarity shifting phenomena.

important trigger words like 'however' and 'but' may be considered.


A nice geometric interpretation of these processes is proposed in (Gaussier et al., 2004), which furthermore introduces variants based on Fisher kernels, Canonical Correlation Analysis and a combination of them, leading to an improvement of the F1-score of 2% (from 0.14 to 0.16) when considering the top 20 candidates.

an alternative approach is of course to use existing bilingual corpora.


Lexicalized HMM can employ dependency relationship among adjacent words.

we use BIO encoding for tag representation, where the non-opinion and neutral opinion words are represented as "O".


This work was partly supported by Chinese NSF grant No.60973104 and No.

it is important to consider the syntactic structure in the review mining task.


Table 2 shows experimental results using model parameters induced from MT03 and MT06-nw as development sets with the same settings as in Table 1.

next we present a method for building a development set on demand based on test data T .


2) autoparse(test) consistently outperforms autoparse(t&t) on both the abstracts and the full papers subcorpora.

it is worth noting that the GTB1.0 corpus includes all the sentences in the abstracts subcorpus of the Bioscope corpus.


It also shows that our final system significantly outperforms the state-of-the-art ones using a chunking approach, especially on the abstracts and full papers subcorpora.

table 2 shows the features in better capturing the details regarding the argument candidate and the negation signal.


This also has the effect of moving subdomain-specific objects and processes from verbs and nouns to adverbs.

they set out restricted ontologies and frequent cooccurrence templates for the two domains and discuss the similarities and differences between them, but they do not perform any quantitative analysis.


This assumption of representativeness is understandable because linguistic annotation is labour-intensive and it may not be worthwhile to produce annotated corpora for multiple subdomains within a single discipline if there is lit-tle task-relevant variation across those subdomains.

the results of Pearson correlation and JSD show similar trends, and due to its specific design for comparing distributions we only report the latter.


(2005), such as the lemma/POS tag of the current/previous/next token, the lemma of predicate and its combination with the lemma/POS tag of the current token, the voice of the predicate (active/passive), the distance between the current token and the predicate, the relative position of the current token to the predicate, and so on.

as shown in Table 1, our system performs much better than the baseline on the goldstandard dataset in terms of all metrics.


More recently, Chiang (2010) has shown that ("exact") tree-to-tree translation as parsing achieves comparable performance with Hiero (Chiang, 2007) using much fewer rules.

given a natural rule the following deductive step grows an item in the chart by the rule where PP 1,3 denotes the recognition of the nonterminal PP spanning from the substring from position 1 through 3 (i.e., yu shalong in Figure 2), w 1 and e 1 are the score and translation of the first antecedent item, respectively, and the resulting item score is calculated as: 5 As the PCFg probabilities of natural rules are fixed during decoding, they can be pre-computed and stored in the rule table.


Our work also has connections to joint parsing (Smith and Smith, 2004;Burkett and Klein, 2008) and bilingually-constrained monolingual parsing (Huang et al., 2009a) because we use another language to resolve ambiguity for one language.

we thank Liang Huang, Hao Zhang, and Tong Xiao for discussions on synchronous binarization, Haitao Mi and Hao Xiong for providing and running the baseline systems, and wenbin Jiang for helping us train parsing models.


In these cases, AM-MNR role information lead to a correct state event recognition.

this is due to the fact that time-Bank contains very few examples of these classes.


Focusing on TimeML, semantic roles have been applied to temporal expressions recognition (Llorens et al., 2009), and temporal links classification (Hagège and Tannier, 2007).

the learning process was done using CRF-L2 algorithm and hyper-parameter C=1.


The aim of this task is to cluster documents on the same topic, that is to say, the same event.

propBank was applied in our system due to the high coverage it offers in contrast to FrameNet.


More recently, TimeML (Pustejovsky et al., 2003a) presented a rich specification for annotating events in NL text extending the features of the previous one.

recall improved by 6% and 10% for recognition and classification respectively.


Qualitative results demonstrate the utility of integrating opinions based on structured ontology as well as the generalizability of proposed methods.

formally, the input is a subset of selected aspects A ; each A i ∈ A is aligned with a set of relevant opinion sentences We define a coherence measurement function over sentence pairs Co(S i,k , S j,l ), which is set to 1 iff S i,k appears before S j,l in the same article.


However, these methods suffer from the problem of producing trivial aspects.

we thank the anonymous reviewers for their useful comments.


This characterization is automatic, cheap and has the advantage of being language-independent.

they generally require language-specific tools and annotated data, making such approaches infeasible for many languages.


(2007) successfully used a POMDP for dialogue control in a ticket-buying domain in which the objective was to fix the departure and arrival places for tickets.


we asked a third-party participant, who was neither a listener nor a speaker in our dialogue data collection, to evaluate the user satisfaction levels of the collected dialogues.


Second, the decision criterion is entailment rather than relevance.

in RTE-3 (Giampiccolo et al., 2007), where some paragraph-long texts were included, intersentential relations became relevant for correct inference.


To determine entailment, BIUTEE performs the following main steps: Preprocessing First, all documents are parsed and processed with standard tools for named entity recognition (Finkel et al., 2005) and coreference resolution.

a global term that occurs in the hypothesis is matched in each sentence of the document, regardless of whether the term explicitly appears in the sentence.


Event extraction performance is evaluated using the evaluation script provided by the BioNLP'09 shared task organizers for the development data set, and the online evaluation system of the task for the test data set 2 .

we note that the parsers may produce more fine-grained word segmentations than that of the gold standard: for example, two words "p70(S6)-kinase activation" in the gold standard tree (Figure 6 (a)) is segmented into five words by Enju ( Figure 6 (b)).


However, basic dependencies were found to consistently provide best performance also for the other parsers 6 .

the BioNLP'09 shared task involved documents contained also in the GENIA treebank (tateisi et al., 2005), creating an opportunity for direct study of intrinsic and task-oriented evaluation results.


Once other NLP tasks, like static relation (Pyysalo et al., 2009) or coreference resolution, become good enough, they can supplement or even substitute some of the proposed rules.

an entity-phrase rule simplifies an entity-containing region without changing the truth-value of the relation.


We observe that our method projects the annotations associated with nouns fairly well.

we show that our method generates reasonably good annotations with an accuracy of 92% on short structured sentences.


Predicates can be simple predicates that lie within the chunk boundary or complex predicates when they span across chunk boundaries.

lexicon lookups will fail for Out-Of-Vocabulary words.


2005) that labels PERSON, ORGANIZATION, and LOCATION entities.


If verb belongs to the "Favorable attitude" class and the polarity of phrase is not neutral, then the attitude score of the phrase is intensified (symbol '^' means intensification): Along with modal verbs and modal adverbs, members of the "Communication indicator/reinforcement of attitude" verb class also in-dicate the confidence level or degree of certainty concerning given opinion.

to the rules of "Object-centered (oriented) emotional state" subclass, which ignore attitude features of a subject in a sentence, the rules elaborated for the "Subject-driven change in emotional state (trans.)"


n , n represents an arc from n to n. two-step process.

we considered them as immediate neighbors to each other.


When a global error occurs, it is usually meaningless to further classify the error, which means that this category excludes the other two.

in addition, since a separate question model is available for C&C, this was also evaluated on ObQ sentences.


Since the dependency representation used in the evaluation corpus is based on the Stanford typed dependency scheme (de Marneffe et al., 2006), we opted for using the WSJ section of the PTB, converted to Stanford dependencies, as our primary source of training data.

it can be seen that MSTParser and Malt-Parser perform below C&C and Enju, but above the other parsers, and that MSTParser achieves the highest score on SbEm sentences and MaltParser on SbRC sentences.


The automatically built training data was noisy and its size was much bigger than manually prepared training data in WebSet.

further if multiple lexico-syntactic patterns support acquisition of hyponymy relation candidates, these candidates are more likely to be actual hyponymy relations.


A naïve solution to this task is to compute similarity values |V | times, i.e., between x and every string y ∈ V .

these values indicate that the proposed algorithm can solve τ -overlap join problems by checking only 50.3% (IMDB), 53.6% (Web1t), and 51.9% of the total inverted lists re-  Numerous studies have addressed approximate dictionary matching.


The expert conditional distributions are defined P α (y|x, λ α ) using the usual conditional random field formulation: and each feature function f α,k (y j−1 , y j , x, j) is either a state function s k (y j , x, j) or a transition function t k (y j−1 , y j , x, j).

humans are good at this: they have an amazing ability to predict, at a micro-level, the actions of an interlocutor (Bavelas et al., 2000); and we know that better predictions can correlate with more empathy and better outcomes (Goldberg, 2005;Fuchs, 1987).


2 Propositionalized constraints have previously been used with integer linear programming (ILP) using binary {0, 1} values corresponding to {f alse, true}, to find an (exact) consistent truth assignment minimizing some cost and solve a global inference problem, e.g.

without prior knowledge the fact-finders do very poorly against our test set of 694 British words, predicting American spelling instead in accordance with the great majority of authors (note that accuracy from an American perspective is 1−"British" accuracy).


for example, "retired science teacher," would be represented as: (NP (JJ retired) (NN science) (NN teacher)) Vadas and Curran (2007a) annotated NP-internal structure by adding annotations whenever there is a left-bracketing.


Next, we extract N -grams from the foreground corpus that have significant frequencies compared to the frequency of the same N -grams in the background model and its individual terms in the foreground model.

we can optimize the keyphrase coverage as described in Algorithm 1.


Figure 1 shows the flow chart of MST parsing, where p 0 .word, p 0 .pos denote the word and POS tag of parent node respectively.

for efficiently pruning candidate templates, each vertex in template tree is restricted to have exactly one template unit (except root).


Here we use a simple greedy strategy, for the vertexes sharing a common father, the most frequent common unit is extracted as new vertex.

speeding up feature extraction is critical especially when large template set is used for high accuracy.


A promising way of overcoming this weakness is to include n-grams, generalizing the bag-ofwords model into a bag-of-phrases model (Baccianella et al., 2009;Pang and Lee, 2008).

in addition, the lexicon provides the prior polarity of the opinion roots.


To know if semantic information is useful for emotion recognition, we have used probabilistic latent semantic analysis (PLSA) (Hofmann, 1999) to cluster words and sentences.

in section 4, the notion of "word emotion components" is proposed and the performance of using encoding feature is presented.


As shown in table 2, when we only use Word Feature(WF), the F-value of task (a) achieved a high value (96.3).

in section 2, based on Ren-CECps and MaxEnt, an exploration of using contextual feature for Chinese word emotion recognition is described.


", it would be helpful for a QA system to know which NEs are cities.

we create four capitalization features to determine whether NP i IsAllCap, IsInitCap, IsCapPeriod, and IsAllLower (see Bikel et al.


This section describes the details of the training process.

collective classification has not been extensively applied in the NLP community, and we hope that our work can increase the awareness of this powerful technique among NLP researchers.


On the 24k test data, the MIN-GREEDY method achieves a high tagging accuracy comparable to the previous best from the IP method.

the node and edge labels are also illustrated in the graph.


This annotator also defined the frames (word senses) used in our experiment.

we apply AL methods for learning to assign semantic frames to predicates, following Erk (2005) in treating frame assignment as a Word Sense Disambiguation task.


(2005) and Roth and Frank (2009) on generating NL directions used hand-crafted heuristics.

the remainder of this paper is structured as follows: In Section 2 we discuss related work.


Our builder constructs a layered ontology from scratch.

when dealing with a multiple-domain corpus, the builder usually suffers from the following 2 problems: First, the ontology conceptual model becomes imprecise in the definition of common concepts that are semantically lopsided in different domains.


Secondly, it permits the identification of unseen morphemes.

let M = {m i |1 ≤ i ≤ |M|} be a set of morphemes, let f i be the frequency with which morpheme m i occurs in a M-corpus of morpheme sequences, let v i = (m i , f i ) for 1 ≤ i ≤ n, and let f i, j denote the number of morpheme sequences in the corpus in which morpheme m i is followed by morpheme m j .


This criterion is based on the Bayesian Information Criterion (BIC) and Jensen-Shannon divergence (Li, 2001).

the algorithm needs to be stopped before getting the initial Mcorpus (since no merging is possible).


Precision measures the proportion of metaphorical expressions that were tagged correctly among the ones that were tagged.

fass himself indicated a problem with the method: it detects any kind of non-literalness or anomaly in language (metaphors, metonymies and others), i.e., it overgenerates with respect to metaphor.


This research is funded by generosity of Cambridge Overseas Trust (Katia Shutova), Dorothy Hodgkin Postgraduate Award (Lin Sun) and the Royal Society, UK (Anna Korhonen).

the scope of our experiments is the whole of the British National Corpus (BNC) (Burnard, 2007) and the domain of the expressions we identify is unrestricted.


Given a set of subsumption relations, multiple Layered-DAGs may be constructed.

then, we propose three ways to discover the subsumption relations, through tag-tag, tag-word and tag-reason co-occurrences respectively.


Computational robustness all recommend the the metric as a strong and useful measure -particularly when evaluating unsupervised morphology analysis systems which, lacking access to labeled training data, are uninformed of the labeling standard used in the answer key.

the same one-to-one matching restrictions penalize proposed analyses that do not conflate allomorphs of the same underlying morpheme, whether those allomorphs are phonologi-cally induced or not.


Given a representative segment of multiparty task-oriented dialogue, DSARMD-1 automatically classifies all discourse participants by the degree to which they deploy selected social language uses, such as topic control, task control, involvement, and disagreement.

we calculated precision and recall of assigning a correct degree of disagreement to each pair of speakers who are members of a group.


Unlike Topic Control, which is imposed by influencing the subject of conversation, Task Control is gained by directing other participants to perform certain tasks or accept certain opinions.

agenda Control is defined as efforts by a member or members of the group to advance the group's task or goal.


To explore its effectiveness in SMT systems, we also manually annotate sub-tree alignment on automatically parsed tree pairs and perform the noisy data evaluation.

some initial statistic of the automatically parsed corpus is shown in Table 2.


Recent research on tree based systems shows that relaxing the restriction from tree structure to tree sequence structure (Synchronous Tree Sequence Substitution Grammar: STSSG) significantly improves the translation performance (Zhang et al., 2008).

the baseline method proposes two score functions based on the lexical translation probability.


Given a bilingual tree pair and , { } is the source tree consisting of sub-trees，where is also the number of nodes in the source tree { } is the target tree consisting of sub-trees, where is also the number of nodes in the target tree .

we change the sum operation into max, which is easy to be normalized.


(2008) can be improved by inference from unlabeled data.


Two of these categories: LCP (localisers) and M (measure words) have variously been argued to be special sub-classes of nouns (Huang et al., 2008).

these are NN (common nouns; 16,552 tokens), NR (proper noun; 8458), VV (verb; 6879), CD (numeral; 1814) and JJ (adjective; 1257).


These principles include complement/adjunct marking: allowing the recovery of predicate-argument structure; limited semantic role marking: the annotation of modifier phrases with semantic roles; covert argument marking: the retention of traces of arguments deleted through pro-drop; and NP internal structure: bracketing of NP structure where the intended interpretation is clear.

based on our overgeneration criterion, we decided to represent these as atomic categories.


Document pairings involving all languages are identified simultaneously.

the words and phrases that are most discriminatory for the purposes of information retrieval and duplicate detection are the relatively rare ones, precisely those that are less likely to be translated well by the baseline translation system.


(2001) classify them as a subtype of lexicalized phrases and flexible expressions.


The recognition of light verb constructions cannot be solely based on syntactic patterns for other (productive or idiomatic) combinations may exhibit the same verb + noun scheme (see section 2).

on the basis of the corpus developed, we plan to design an algorithm to automatically identify light verb constructions in Hungarian.


machine translation and information extraction may profit from the successful integration of such an algorithm into the system, which we plan to develop in the near future.

the presence of an argument in the accusative does not imply that the noun + verb combination is a light verb construction.


Another limitation apparent from the first rule above (the movement of verbs in a verb phrase depends on the previous phrase being a noun phrase) is that the automatic reordering rule for a node's children depends only on the children of that node and not a larger context.

difficult are nominal phrases from specialized domains that involve challenging terminology, for example: "group reference attribute" and "validation checking code".


In close collaboration with two companies from the semantic technology field, we identified eight high-level subtasks in the overall process as explained in the following.

7 We could not measure precision for these since not all sentences and entities are annotated in the corpus, as mentioned in Section 4.


Unlike us, the authors put much emphasis on retrieval aspects and applied dependency grammar parsing to identify market statements.

50 suppliers provide multi-touch screens, and that number is likely to rise.


Generic summary of each article was required to be created for task 1, and generic summary of each document set was required to be created for task 2.

the ROUGE-1 score of UnifiedRank is higher than that of the best participating systems and the ROUGE-2 and ROUGE-W scores of Unifie-dRank are comparable to that of the best participating systems.


Finally, the algorithm converges and the local saliency score and global saliency score of each sentence are obtained.

for example, Wan (2008) proposes to use only cross-document relationships for graph building and sentence ranking.


Hence, there is a need for more effective approaches to automatic vandalism detection.

for the syntactic n-gram model, we submit a version of the segment where each original word is associated with its POS-tag.


In the example in Figure 2, the vandalism diff text will probably score low, because although it shares an overlap bigram "and roll" with the phrase "rock and roll" in training text, once we apply the shallow syntactic n-gram modeling method, the POS tag bigram "and/CC roll/VB" in diff text will be distinguished from the "and/CC roll/NN" or "and/CC roll/NNP" in the training data.

their average recall is not very high and they can be easily fooled by unseen vandalism patterns.


Input: A set of syntactic trees for sentences (S); the depth range (D) Output: A set of sub-tree patterns extracted from S 1: P atterns = {} 2: for all Syntactic tree T ∈ S do 3: Nodes ← Top-down level order traversal of T 4: for all node n ∈ Nodes do 5: Extract subtree p rooted under node n, with depth within the range D 6: p ← generalize(p) 7: P atterns.add(p) 8: end for 9: end for 10: return P atterns Although Conditional Random Fields (CRF) is good sequential learning algorithm and has been used in other related work (Cong et al., 2008), here we select Support Vector Machines (SVM) as an alternative learner.

the state-of-the-art retrieval models include the vector space model (Duan et al., 2008), language model (Duan et al., 2008;Jeon et al., 2005), Okapi model (Jeon et al., 2005), translation model (Jeon et al., 2005;Rie-zler et al., 2007;Xue et al., 2008), and syntactic tree matching model (Wang et al., 2009).


The mining and leaning process is fully automated and requires no human intervention.

we take the wrong patterns and utilize them to detect questions in the irregular forms.


It is not straightforward to implement reordering in the current model because it breaks the word-order constraint which admits tractable forward-backward style dynamic programming.

syntactic-matching conditions listed below).


Inspired by the above discovery, accordingly, we convert all the binaryvalue features into their corresponding realvalued features.

compared with the original joint model, the new joint-plus approach does not show much improvement, regardless of the significant improvement made by the discriminative-plus model, as the additional benefit generated by the discriminative-plus model has already covered by the generative approach (Among the 6,965 error words corrected by the discriminative-plus model, 6,292 (90%) of them are covered by the generative model).


This work confirmed our early postulation that document LoA might not be tailored for lexical level tasks, which might require lower LoAs for more fine-grained co-occurrence knowledge.

for the pair forest and woods, it might be easy to find words satisfying one but not both constraints.


We exploit pattern-based and character-based methods to infer sentiment expectation of nouns, and then determine the polarity of DSAAs based on the nouns.

different from the traditional classification scheme, we propose sentiment expectation and classify nouns into two categories: positive expectation and negative expectation.


(2006) is similar to ours because they also define the sentiment score of a word by its composite characters.

we adopted the same algorithm with wan (2008), and also used Sentiment-HowNet.


The opinions expressed on the Web reflect the common cognition shared by collection of people in a culture.

here the noun (n) in these patterns was instantiated by 9,468 nouns in our collected data.


3 Using all rule table, our joint method significantly outperforms the best single system SF by +1.96 and +1.66 points on MT04 and MT05 respectively, and also outperforms the lattice-based system by +1.46 and +0.93 points.

recently, many researchers have shown that replacing 1-best tokenizations with lattices improves translation performance significantly (Xu et al., 2005;Dyer et al., 2008;Dyer, 2009).


And 46 unique words appear only in the joint method, where most of them are named entity.

we tokenize the entire span into a single word with a translation that is the copy of source side.


The newly constructed emotion lexicons were also reported as resources together with our paper.

in this way, the fourth weighted matrix was formed.


The above section introduced a method to rank words with a few seed emotion words.

in this dictionary, the words in a synonym set are presented in one line and separated by spaces, so there is no need to perform word segmentation and POS tagging.


Here also, the improvement maximises around the 83% threshold point where the two baselines intersect.

our research aims to establish a model that estimates how likely an ambiguity is to lead to misunderstandings.


Nocuous ambiguity occurs when a linguistic expression is interpreted differently by different readers in a given context.

disambiguation is not always appropriate or even desirable (Poesio and Artstein, 2008).


The phrasal scores assigned to the new phrase pairs were set to 1; the lexical scores were computed from a word lexicon trained over both the baseline data resources and the parallel web data.

individual language models were trained for each data source and were then linearly interpolated with weights optimized on the development set.


Using the annotated datasets described in 2.2, we built an upper model and a lower model, corresponding to the schema in Fig 2.

first, the variation phenomenon in Chinese involves at least one more major variant, that is, the preverbal word order, which significantly increases the complexity of the phenomenon.


In measuring the substitutability of words, the co-occurrence information between a target word (the gap) and its context words is commonly used in statistical approaches.

the mutual information can measure the cooccurrence strength between a near-synonym and the words in a given context.


EAM with k = 1 has 1 degree of freedom corresponding to 1 parameter, the same as BAM.

note that for an ideal perfectly performing CDS model we can write z = (xy).


For example, the word philosophy derives from two Greek words philos (beloved) and sophia (wisdom).

x and y are the two distributional vectors of the words x and y. z is the expected distributional vector of the composition of x and y.


In opinion mining of product reviews, one often wants to produce a summary of opinions based on product features/attributes.

eM may be able to correct some of the mistakes.


In the semi-supervised category, the methods are further classified into un-constrained, hardconstrained, and soft-constrained methods.

we can do better since the EM algorithm only achieves local optimal.


As for the parsing performance, our baseline PW parser using the PW supertagger was 0.23% below the Enju parser.

the parsing time of the Ft parser was very close to that of the PW parser (108s vs. 106s), which was also efficient.


2009) and NLPR_KBP1 (Han and Zhao 2009) use similarity ranking approach; Stanford_UBC2 (Agirre et al.

we further leverage on the wikipedia documents to provide other information which is not available in our generated corpus through a domain adaption approach.


For pre-processing, we perform sentence boundary detection and Chunking derived from Stanford parser (Klein and Manning, 2003), Named Entity Recognition using a SVM based system trained and tested on ACE 2005 with 92.5(P) 84.3(R) 88.2(F), and coreference resolution using a SVM based coreference resolver trained and tested on ACE 2005 with 79.5%(P), 66.7%(R) and 72.5%(F).

our experiment results show that both proposed knowledge sources are effective for entity linking.


CSIDM-200804 partially funded by a grant from the National Research Foundation (NRF) administered by the Media Development Authority (MDA) of Singapore.

our proposed approach employs instance weighting and beam search to maximize the Fmeasure of the evaluation metric during training.


We conduct experiments using a query log of a commercial Chinese search engine Baidu, from which we extracted about 2.7 million pairs of paraphrase Q-T, 0.4 million pairs of paraphrase Q-Q, and 0.4 million pairs of paraphrase T-T.

this distribution is in line with that reported in (Zhao et al., 2008).


Hence we will improve the method in our following experiments.

three kinds of paraphrases can be extracted with our method, which include (1) query-title (Q-T): a query and a document title that users clicked on; (2) query-query (Q-Q): two queries, for which users clicked on the same document title; (3) titletitle (T-T): two titles that users clicked on for the same query.


The problem is even worse when the source sentences get longer and more complicated.

the results show that: (1) although the candidate paraphrases acquired by Mt engines are noisy, they provide good raw materials for further paraphrase generation; (2) the selection-based technique is effective, which results in the best performance; (3) the decodingbased technique is promising, which can generate paraphrases that are different from the candidates; (4) both the selection-based and decoding-based techniques outperform a state-of-the-art approach SPG (Zhao et al., 2009).


Let be the set of preference judgments, where x i and x i ' are the feature vectors for two pairs of surface-forms and Wikipedia-entry, l(x i ) and l(x i ') are their absolute judgments respectively.

in our experiments string similarity was based on word overlap.


• Web search engine queries resulting in user clicks on a Wikipedia article are taken as alternative surface forms for the entry.

attempting to address this point, we see that the F1 values of WikiRes on the MSNBC test set and on the Yahoo!


But if taking sentence length into consideration, we can find that the TCT-based decoder is actually relatively weak.

plete parsing on the span [start, end] and gener-atesĤ k (s).


We also conduct experiments on two Chinese treebanks: CTB and TCT.

such corpora often follow different annotation standards and/or even different linguistic theories.


With MEW strategy, the F 1 score is improved about 0.5% further.

the inequalities in equation 3satisfy the No duplication constraint.


We acquire these weights by minimizing an error function on the development set.

suppose that there are n system outputs to be combined, with the i-th output's weight to be w i .


Hence, we adapted an in-house tokenizer (using cascaded finite-state rules) to the CoNLL task, run two PoS taggers in parallel, and eclectically combine annotations across the various preprocessing components-predominantly giving precedence to GENIA lemmatization and PoS hypotheses.

during development, we trained cue classifiers using a wide variety of feature types, both syntactic and surface-oriented.


Using system cues, scope resolution on the held-out data scores at 55.75 F 1 .

(6) {the unknown amino acid, if it is encoded by a stop codon, is unlikely to exist in the current databases of microbial genomes}.


By focusing on patterns, URE benefits in two ways: On the one hand we show that an informed feature generation strategy can markedly reduce the amount of underspecified and ambiguous patterns in the pair-pattern matrix, thereby significantly improving clustering approaches.

this step yields a list of core tokens likely to be relevant to the relation expressed between the two entities.


The other neutral example is a negative sentence that is not relevant to assessing the POV of the article towards the target -e.g., because it talks about historical background that the target is not involved in.

the system prediction is -.217 whereas the true score is .333.


Lemmatization has been used widely in classification problems due to its ability to generate one form that matches many other related forms (Al Ameed et al., 2005).

first, our error analysis showed that most errors in predicting POV difference were due to our simple representation of sentences: bag of words.


The question as to how to automatically assess whether a piece of text written in objective language represents a positive or negative POV of the subject matter and to what extent has not been addressed before.

we found that international personalities are viewed in more positive light in the Arabic Wikipedia than in English.


Searching for good tokenizers, we limited our options to the ones that support Unicode.

this section is not meant to review the literature of named entity recognition research as this is already available in (Nadeau and Sekine, 2007).


In Section 3 we describe the ontology and the existing parallel resources, which were used for generating the translation and language model.

we translated the aforementioned German GAAP ontology, which holds 2,794 labels 15 .


Work in parse correction consists on the creation of corrective models to solve difficult ambiguities, such as PP-attachment ambiguity in English.

the third source is the result of directly querying the web.


The gold standard comprises ambiguous instances of noun phrases, which can either be subject or object.

case-marking for subjects of intransitive verbs and objects of absolut i ve = sub jec t o f int r ansi t i ve ver bs o b jec t o f t r anst i ve ver bs er g at ive = sub jec t o f t r ansi t i ve ver bs Figure 1: Two cases in Basque, and their respective syntactic functions depending on the transitivity of verb.


This part is useful for evaluating the choice of the learning method and the feature set.

n-gram features were based on language models of order 5, built with the SRILM toolkit (Stolcke, 2002) on monolingual training material from the Europarl (Koehn, 2005) and the news (Callison-Burch et al., 2011) corpora.


As machine translation (MT) comes closer to the everyday use, the need to predict the quality of machine-translated text is being of intense interest.

in order to be able to draw a fair amount of conclusions in a decent amount of time, we followed an incremental approach: first, we devised some feature sets that have shown to perform well in previous work 3 and used them for learning and testing with the default parameters of all the available methods.


This account 5% to the taxonomically related semantic triples and 0.03% to the total semantic triples extracted.

but, while auditing systems have made large contributions in identifying the inconsistencies and ambiguities, the large volume and number of biomedical knowledge sources and many inconsistencies and ambiguities make them difficult to circumvent the inherent problems of the resources (Erdogan, 2010;Friedman et al, 2001).


types Remove the ancestor STs, if any Hierarchical relationship inconsistencies occur in cases where T 1 becomes an ancestor of T 2 in relationship conditions if C 1 and C 2 are related taxonomically in MT (C 1 isa C 2 ), and C 1 is in T 1 (C 1 isa T 1 ), C 2 is in T 2 (C 2 isa T 2 ).

in evaluator A, 87 are accepted and 13 are unaccepted.


Le succès en pratique des systèmes exploitant ces informations sémantique, dépend fortement de l'exactitude et de la qualité des tuples sémantiques.

semantic types in each semantic group, semantic concepts in each semantic type, and semantic atoms in each semantic concept are extracted to have explicit representation of the structure.


(Axelrod et al., 2011) presents a technique of using the difference in cross-entropy of the supplementary sentence pairs on 'in-domain' and 'out-of-domain' datasets for ranking and selection by thresholding.

in a similar approach to the lexical table merging, every entry in the new (incremental) batch phrase-table, is compared against the older (baseline) phrase-table and the shared phrase pairs are updated by the formulae in (2): c bl (e)+c inc (e) + φ inc ( f |e) × c inc (e) c bl (e)+c inc (e) 2where φ bl , c bl , φ inc and c inc indicate the baseline phrase translation probability, baseline phrase count, incremental phrase translation probability and incremental phrase count, respectively.


In order to create an SMT system for a specific domain which does not have sufficient parallel training data, domain adaptation methods are necessary to best utilise supplementary parallel training data from available out-of-domain or related-domain corpora.

the SMt system used in our experiments is based on the standard phrase-based SMt toolkit: Moses .


To alleviate this issue, the source data of the devset selected for the set of experiments reported here, is randomly chosen from a large collection of the target domain data and is ensured to be truly representative of the the target domain in terms of meta-statistics.

when compared to the Full scores, the TQS method provides statistically significant improvements for nearly all the cases.


A multitude of text similarity measures have been proposed for computing similarity based on surface-level and/or semantic features (Mihalcea et al., 2006;Landauer et al., 1998;Gabrilovich and Markovitch, 2007).

only a low similarity score  can be computed between the additional material in the newspaper article and the original source, and the overall similarity score decreases.


Furthermore, only 27% of the internet users claim English as their native language (Miniwatts Marketing Group, 2011).

since almost all languages are collocated with each other, we pruned some edges to make the figure clearer.


In the structured approach, websites containing parallel text are typically identified using search engine APIs and the crawler proceeds to identify new websites based on the preceding ones (Resnik and Smith, 2003;Shi et al., 2006;Utiyama et al., 2009;Hong et al., 2010).

one can potentially harvest parallel text for all of these language pairs and subsequently exploit it for machine translation.


Multilingual parallel text extraction has been specifically addressed in (Uszkoreit et al., 2010).

we plan to conduct experiments on the other 24 language pairs not reported in this work as part of future work.


Even though the RBMT system was tuned to the domain of the TM via domain specific lexical resources (Section 3.2), most of the errors appear to be due to the RBMT system's inability to pick the right term for the technical domain data set.

we will research ways to further refine statistical post-editing techniques for both RBMT and SMT systems.


The frequency of the choices in the revised annotation is summarized in the last column of Table 2.

units that enter the coreference relations sometimes do not correspond to a continuous subtree of a tree.


The entries in the dictionary include stem forms, alternate forms, glosses in English and Nepali, as well as some grammatical information.

many other constraints, particularly those concerned with semantic composition, are inherited from the matrix core grammar.


The local economy is centered around agriculture, both for subsistence and for trade.

even broad coverage grammars rely heavily on the size and quality of their lexicons.


shortcomings of n-gram models are obvious: no long-range relations are modeled explicitly, thus n-gram models produce locally readable but semantically incoherent text.


More importantly, we also found a strong interaction between Time and Condition (z=-3.260; p=0.001); i.e., the accuracy of Group B increased faster over time than Group A.

dial Your disc (Van deemter and Odijk, 1997) generated spoken monologues about classical music, with the aim of generating engaging texts, attempting to keep its users amused by focusing on the expression of unusual content.


The final corpus comprises a total of 94,089 phonemically transcribed utterances and consists of six distinct sub-corpora, each corresponding to the CDS directed at one of the six children.

in this paper, we look at a basic point of variation, namely the actual size of the input to the learner.


The collocation models assume that sentences are sequences of phrases that, themselves, are made up of individual words.

this much is clear from the ease with which syllable structure can be incorporated into the models.


The context itself can be defined in different ways, such as an n-word window surrounding the target word.

(G-T) Gold vs. Target: This dataset is composed of the remaining lexical simplification examples of our gold standard (14 instances) and their target words.


We could set up more evaluation data to compare the system and the baselines.

three annotators with no previous annotation experience performed the tests using an on-line form.


Our results are comparable with the lexical simplification system for English presented in (Biran et al., 2011), whose frequency baseline for English target words (medium frequency) is very similar to ours (42.86%).

53.84% of the manually substituted target words were not simplified by LexSiS, and 78.57% of these manual substitutions were found to be simpler synonyms by the annotators (see Table 4).


The generative story for a single utterance entails sampling the following: 1.

even predominantly task oriented dialogue (e.g.


Gibbs sampling is used to sample the posterior distribution p({c (i)  where U is the total number of utterances.

we define social acts as pragmatic speech acts designed to give insight into the socio-cognitive processes that individuals unconsciously go through when communicating in dialogue.


While most dialogue acts have some social overtones, they fail to adequately interpret the speaker's social goals.

grosz and Sidner (1986) posit a structural approach to dialogue understanding where dialogue is broken into three constituents: linguistic structure, intentional structure, and attentional state.


ki 'girl' is already taken to be in the state of remembering the story (and not actively entering a state of remembering the story).

mousing over a colored box reveals the relative frequency of the N+V CP.


'to remember'), South Asian languages tend to use combinations of more than one element to express an action (e.g., memory+do = 'remember').


Class A refers to examples as in (1) and this type seems to encompass what is known as psych-predications, i.e, actions of remembering, thinking, feeling, etc.

one is a normal space, the other is zero-width non-joiner (HTML code: ).


In a second step, we constructed a list of stop words from various sources.

our "blind" extraction of patterns yielded new insights into what kinds of CPs are highly frequent in newspaper corpora in addition to N+V CPs and which therefore need to be paid attention to with respect to NLP applications and the creation of language particular resources.


We found that a number of five clusters minimized the average distance between the nouns and the cluster centers.

we looked for a total of 238 different forms of our set of four verbs, but normalized the different inflected versions to just the stem form for purposes of bigram storage.


This paper presents an investigation into whether fine-grained information about the distributional properties of nouns in N+V CPs can be identified by the comparatively simple process of extracting bigrams from a large "raw" corpus of Urdu.

the visual analysis allowed us to be able to quickly assimilate information about complex interrelationships in our data: which types of verbs does the word in question occur with with what level of frequency and how does this compare to other words in the list?


Extrapolating to a larger training set, they argue that this result is consistent with the findings reported by Koppel et al.

in both cases there were more than 100 different topics, and none of them matched between iCLE used for training and NOCE/USE/HKUST used for testing in the cross-corpus setup.


In that light, the Open-Class-POS-based n-grams appear attractive since they replace many of the topic-specific meaning distinctions with POS-tags.

hence, they are unlikely to be relevant for the given NLI task.


As mentioned in Section 3, we thus conducted nine further experiments.

the findings of Brooke and Hirst (2011) suggested a high topic bias in the ICLE data.


Over-generated questions can be ranked using different approaches such as statistical ranking methods, dependency parsing, identifying the presence of pronouns and named entities, and topic scoring (Heilman and Smith, 2010a;Mannem et al., 2010;McConnell et al., 2011).

wSK's value is just the inner product of the two vectors.


For the development data, the best precision 84.2% is achieved using a threshold equal to 1.9.

(2009), we use a similar method to calculate a score based on the n-gram counts before and after a potential deletion, as demonstrated in Table 2.


Next each of the n people is given a distinct point on the curve.

this is only a preliminary grammaticality check and does not guarantee sentence fluency.


For example, synonym substitution, paraphrasing and translation can be applied to an embedding method which reconstructs the secret message as concatenating codewords that are directly associated with a choice.

we have ignored the possibility of computational steganalysis and steganographic attacks, such as detecting, extracting and destroying the hidden message (Fridrich, 2009).


The word ordering realisation system plays a crucial role in the proposed steganography method as it largely determines the security level and payload capacity of the stegosystem.

since the inter-annotator agreement on the collected human judgements is not 100%, as shown in section 3.2, it is not clear whether the 90% precision achieved by the classifier really means that the remaining 10% sentences (false positives) would be viewed as suspicious in a real steganography setting.


The rank of a permutation in the sorted list is converted into a binary string, and the lowest s bits are the secret bits carried by that permutation, where s is the pre-fixed payload.

next, since there are studies using automatic evaluation metrics to evaluate the security level of a stegosystem as described in Section 1, we observe how well the BLEU score of a permutation correlates with the human judgement.


A research area that relates to the proposed permutation checking method is realisation ranking (Cahill and Forst, 2010;White and Rajkumar, 2012) where a system is given a set of text realisations and is asked to rate each text in the set.

in addition, if the difference between a permutation and its original sentence is only a single word movement, we add the POS tag of the moved word to the indicator function, so the feature becomes "EDiST_1-POS".


But since never appears in the training set, the word feature is useless as far as classification is concerned.

we thank the three anonymous reviewers for their invaluable comments on an earlier draft of the paper.


For these cases, we use word alignment information, and perform the word alignment and the probability-based extraction methods.

an SMT system fails to capture long-range contextual knowledge due to the limited horizon and the sparseness nature of lexical n-grams.


We simplify a clause by simplifying its children recursively according to the above simplification rules.

bilingual syntactic units are relatively hard to obtain.


We evaluate the quality of obtained sentiment vocabularies intrinsically.

we suppose that gathering a specialized sentiment lexicon for the product meta-domain can be useful for researchers and practitioners.


Moreover such an approach cannot handle neo-classical compounds.

the goal is of course to find the strategy that best helps the alignment algorithm and thus results in a faster convergence and/or better performance.


In summary it is thought that with partial matching, for the entity types examined so far, the core part of the entity was in most cases correctly found.

for a class as complex as phenotypes this is a particular consideration.


In a non-configurational language such as German, which features argument composition, heads combine with complements that are not their own arguments but rather those of a predicate they compose with, i.e., they need to cater for unknown raised arguments by means of underspecified valence lists.

... weil ein Buch 2 er 1 ihm 2 zu kaufen 2 versprach 1 'because he 1 promised 1 to buy him 2 a book 2 .


given we can define the k th chunk ( as: 〈 〉 〈 〉 〈 〉 for some i < j ≤ N and 1 ≤ k ≤ M for a total of M chunks in the query.


The vocabulary containing words  forms is updated as more training examples are seen and used by the feature builder.

interestingly, forms seem to be playing a stronger role in MSQA, since a traditional CRF performs poorly in this case.


For example, Table 5 shows that with γ = 0, Algorithm 1 will select up to 15 different steps for the given choice Ω, most of which seem linguistically questionable.

changes from one form to the other are in bold (information not given in training).


M-M+HMM: Many-to-many aligner with HMM and instance-based segmenter for decoding as reported by (Jiampojamarn et al., 2007).

we identify S-restricted monotone paths with S-restricted monotone alignments in the sequel.


Still, we can come up with the idea that the difficulty of words determines the learners' knowledge of second language words.

this is unrealistic because some learners have special interests.


However, unlike word difficulty parameters, little information is practically available for learners.

for example, if there are 15 learners in a data set, it is trivial to determine the words with the highest variance of y as those that 8 learners knew and 7 learners did not, or 7 learners knew and 8 learners did not.


Therefore, y variance does not produce any interesting results.

the weight vectors are trained in the in-matrix setting.


There are two kinds of parameters to be trained: In the Rasch model, the subtraction of two parameters a u − d v in Eq.

y variance does not produce any interesting results.


1 Early TFS-based parsing systems like ALE (Carpenter and Penn, 1996) required that every pair of unifiable types have a least common supertype, i.e., joins are uniquely defined wherever they need to be defined.

from (i) and (ii), if m is even then n ≥ 2p + 1, and if m is odd then n ≥ 3p + 2.


ConstRel features are similar to POSRel but use the constituent labels rather than the POS tags of the heads.

we followed the methodology of contribution analysis for phrase structure parses and we manually investigated the sentences of the English development set where the discriminative parsers with and without these features gave different output.


This research was also partially funded by the Ministry of Economy under grant TIN2009-14715-C04-01 (KNOW2 project).

to generate good quality units for the evaluation the informativeness of items was calculated as follows: in f or mat iveness(i t em) = l eng th(i t em t i t le )/av g L t i t le * log(N /count(i t em t i t l e )) +leng where t i t l e, desc, sub j refer to the title, description and subject fields of the metadata, av g L X is the average length of field X over the whole collection, leng th(i t em X ) is the length of that item field text, and count(i t em X ) gives the frequency of that item field text over the whole collection.


LDA has been used to successfully improve result quality in Information Retrieval (Azzopardi et al., 2004;Wei and Croft, 2006) tasks and is thus well suited to support exploration in digital libraries.

these studies often don't answer certain questions about the taxonomy.


One difficulty concerns multi-word expressions that the campaign managers finally chose to annotate both as multi-word expressions and as distinct elements to avoid "linguistic debates".

the first method consists in measuring the residual ambiguity degree by observing the traces left by the annotator during the annotation: the annotation protocol may allow the annotator to annotate with several labels in case of ambiguity, to use an under-determined label, or even to add an uncertainty feature to the chosen label.


various inference techniques have been proposed to estimate the probabilities in LDA, including variational Bayes, expectation propagation, and Gibbs sampling (Blei et al., 2003;Griffiths and Steyvers, 2004).


The only observable entities are the query terms in the source language as shown in Figure 4.

a natural question which arises is whether we need to introduce a new linear combination parameter to choose the two event paths with relative weights similar to (Chinnakotla et al., 2010).


The θ REG is a regularization parameter to penalize the creation of too many rules.


These results, in conjunction with those in Table 1, indicate that the performance of the linearized model was worse than that of the improved method, but similar to those of the original one (Takamura et al., 2005).

the plot indicates that the selected magnetization bifurcates to a finite value for β ≃ 1.3.


they are individually insufficient for correctly classifying most other words corresponding to negligible elements.


Since the corpus used as the source for annotation consists of complete texts that preserve the context of sentences within their discourse structures, the tagged corpus will be potentially helpful to pursue research towards discourse-level dependency parsing, including anaphora resolution and ellipsis determination.

one important concern is that of the correctness of the computational processes used in the morphophonetics routines of the Heritage engine with respect to the Pān .


We remedy the poverty of the memory-less protocol (HTTP) by transmitting parameters summarizing the interaction history in the current session.

the first step was to project the headwords of the SH dictionary onto those of the digitalised Monier-Williams dictionary.


Once an SH word was matched to all the possible MW headwords, the matching values were sorted and the headword with the highest match was marked as the suitable match.

they view is and are as inflectional varieties derived from one common root, and was and were as inflectional varieties derived from another root.


The summary mode greatly improves the robustness of the SL/SH interface, specially for long complicated sentences, where the large number of potential solutions could possibly choke the server.

it is difficult to match the direct meanings as obtained after extracting the meaning text from both the lexicons.


In this paper, we presented a framework to analyze the differences in translation quality between several translation systems based on characteristics of their translation models and hypotheses.

extract the relevant features corresponding to each subdocument.


In our specific application, we are interested in predicting translation quality.

), which allow for less gaps in the target phrases than the discriminative type of alignments.


The success or failure of an algorithm is based on trial and error, guided by the end-to-end automatic translation quality metrics as a measure of performance.

in this case, the feature has a negative weight, which indicates that the longer the phrases in the translation model, the worse our translations are.


We used the baseline translation features in the phrase-based model (Koehn et al., 2003): inverse and direct phrasal translation probabilities and inverse and direct lexical probabilities.

we have a preference for hypotheses that have discontinuous reordering.


The key idea is that ME features connect a word and a class, with larger λ k weights indicating stronger word-class associations.

this appears to be due to the scope and domain of this service, which predicts locations at the world level, whereas the other methods are restricted to North America.


T-Units are defined as "one main clause plus any subordinate clause or non clausal structure that is attached to or embedded in it" (Hunt 1970, p. 4;cf.

the tagged articles, mapped to the Stuttgart-tübingen tagset (SttS) for German, were then parsed with the Stanford Parser for German (Rafferty and Manning, 2008), which comes with a German model trained on NEGRA.


Although it is tempting to apply these kernels to measure the similarity between the dependency (sub)trees surrounding the target words, it should be noted that our goal is to measure the similarity of particular word pairs in two sentences, and not of the sentences or their entire dependency trees.

1, walk probability can be tuned by the model parameters L and γ; e.g., the larger the stopping probability γ, the smaller the probability of taking longer walks.


(1) The sentence was a lead sentence (the first sentence of an article).

to do this, we use Lagrangian relaxation, which is a classical technique for combinatorial optimization.


(2), the length of the compressed sentence shall be not more than L. The advantage of word-based methods is that the methods can more freely select important words in a source sentence than bunsetsu-based methods.

in this way, when b 8 is contained in a compressed sentence, we can ensure that b 9 is also contained in the sentence regardless of whether the bunsetsu are shortened.


That is, the proposed method basically follows the constraints and treats each bunsetsu as a unit.


Lagrangian relaxation is a well known technique for combinatorial optimization and it has recently been successfully applied to various natural language processing tasks (Koo et al., 2010;M.Rush et al., 2010;Chang and Collins, 2011;M.Rush and Collins, 2011).

we first describe word-based methods in detail.


Furthermore, using this equation, we can retain a unit constraint for each bunsetsu that does not contain a compound noun (i.e., for such bunsetsu, Eq.

care must be taken in setting each variable.


Consider the source sentence in Figure  2.

as described in the previous paragraphs, if bunsetsu contain unimportant words, our method prioritizes to violate their unit constraints and drop the unimportant words.


Recall (R) and negative recall (NR) reflect the percent of the relevance/irrelevant documents that can be detected by their snippets.

the improvement on top-ranked documents (ERR@10 and NDCG@10) is higher than MAP.


As an illustration, Table 5 shows two examples of generated snippets.

other methods could be explored in the future, such as SVM-rank, etc.


This categorized lexicon is quite similar to word cluster features in (Ratinov and Roth, 2009), but is more precise.

aDF and MaDF are consistently faster than plain SGD and often reach reasonably good performance after a mere 10 passes over the datasets.


Section 3 describes our proposed method and shows the complete list of features used for training a classifier.

we have 16 features (T1-T16: 'T' stands for title) regarding various aspects of the surface string of a title.


For example, the article of "Nobunaga Oda", a famous warlord in Japan, has section titles such as life, personality, portrait, and policies, which clearly indicate that this article is about a person.

table 6 shows the top 20 ENE types in the created ENE dictionary.


Here, we only use top-level section titles.

we removed seven features: (T3) Character unigram, (T4) Character bigram, (T5) POS unigram, (T10) Last two characters, (T13) Proper noun semantic categories, (T16) Character type construction, and (T7) Infobox attributes.


One possible solution is to introduce a parameter of extraneous word generation probability, p 0 , like IBM model 3 (Brown et al., 1993).

we transformed the pronunciation of the Yahoo!


G=(D, U, T, R ): the related social tagging data that document d and user u belong to; N dc , N uc , N tc : the predefined number of document clusters, user clusters, and tag clusters.

the alternative way is to get the external judgments from several judges and take average of their ratings so that we know that multiple people would consider that this summary is relevant or tailored for the intended user to a certain extent.


However, most of these models, such as TextRank (Mihalcea and Tarau, 2004), as originally developed apply only to homogeneous networks.


Our algorithm is an extension of Co-HITS (Deng et al., 2009), which is limited to bipartite graphs.

similar tweets appear with high frequency in the tweet set.


In this paper, we have proposed to incorporate lexical information in the unsupervised CCG induction.

they are generated from one of the following two unary rules: TOP → N TOP → S In addition, we restrict that: (1) strings containing at least one verb must be sentences, i.e.


This work was partially supported by Grant-in-Aid for JSPS Fellows (22-9719) and Grant-in-Aid for Scientific Research (23240018).

for example, the following axiom maps the verb give to the GIVING frame.


The ILP objective function is as follows.

the function takes two arguments: costs of the propositions backchained on and weight of the assumption.


In addition, we used a resource assigning possible lexical fillers disambiguated into WordNet synsets to FrameNet roles (Bryl et al., 2012).

instead of using world knowledge for generating features in a machine learning framework, we explore inference-based discourse processing.


N-grams which are semantically similar in meaning will cluster close to each other in the latent space forming a word cloud of semantically related terms (Berry et al., 1995).

we also included Computer Science documents in the Science domain.


Given a query, a similarity-based IR system retrieves documents.

readability methods tend to perform poorly on domain-specific texts (Yan et al., 2006) and web pages (Collins-Thompson and Callan, 2005).


Problem mentions generally occur with modifiers and have variegated surface forms.

the difference in such cases is not statistically significant (69.1 vs 69.3 and 58.3 vs 58.4).


We present experiments on 4 different clinical datasets illustrating that our approach outperforms a strong baseline and a state-of-the-art system by a wide margin.

given a document d and a pairwise coreference scoring function pc that maps an ordered pair of mentions to a value indicating the probability that they are coreferential, we generate a coreference graph g d according to the following decision model: For each mention m i in document d, let B m i be the set of mentions appearing before m i in d. B m i = {m 1 , m 2 , ..., m i−1 }.


2 We believe that annotating and classifying citing sentences (as opposed to citations) is not specific enough for tasks like IR and bibliometrics.

in addition to adding new syntactic features (POS-pattern k , see above), they tested ensemble-style self-training to overcome the problem of limited annotated data.


used 12) and limiting the number of cue words to 75 per label.

this approach for acquiring named entities is not domain dependent and can be used to develop a reasonably efficient NER system using lists of tools or resources from any domain.


We have described unsupervised algorithms for string-graph alignment, and two algorithms for automatic SHRG learning given these alignments.

while our system adopts a semantic transfer based paradigm, we learn weighted transfer rules into and from the meaning representation automatically to build a true statistical semanticsbased MT system.


Even though there are only two possible answers, yes or no, such questions can be quite hard to answer.

deepQA's confidence value of the first answer for the most reliable question.


The confidence values, calculated independently for each candidate, estimate the likelihood to be the correct answer (so the sum of confidence values across all answers may exceed 1, reflecting the fact that multiple answers may be correct).

now the question is how to interpret multiple answer lists output by DeepQA to determine the correctness of a given proposition.


We think there are two reasons for this.

in the case of " (ai or ran)," our first step could not make anyone identify the kanji.


One of the major differences between them is the number of target characters.

of human evaluation, we confirmed that distinctive explanations generated by our system outperformed those in existing screen readers.


In this paper, we propose a method for automatically generating distinctive explanations for a kanji, and aim to improve the kanji identification rate.

the screen reader achieved a high identification rate for this kanji, by using the distinctive explanation "Write on top of (tsu-chi, soil)."


Each semantic type belongs to exactly one SG.

the block diagram of the system is illustrated in Figure 4.


One important point to mention is that we only included positive sentences (sentences with at least one phenotype) in our training set, because the number of negative sentences was far greater than positive sentences and it prevented the system from training efficiently.

in the current paper we discuss a machine-learning-and-dictionary-based NER system that recognizes the human phenotype names in molecular biology literature which has been inspired by the rule-based method mentioned above.


NP: A binary feature which indicates whether a token is a part of a noun phrase.

we had to make our own corpus with a sufficient number of sentences.


Table 2 lists examples of the pair-dependency features generated from these parses.

each of the DG schemes performs similarly, with the exception of the Fanse scheme, which provides roughly 0.5% higher F-score than the others.


We use the same experimental settings reported in Nivre et al.

we show how our new DG-derived features substantially improve parser performance by 0.35% to 87.93%, and improve the accuracy of the C&C parser on both short and long-range dependencies.


This feature was not used for the CoNLL scheme (as this scheme does not include labels), but was active for each of the others.

we have improvements of 2.86% and 1.67% over the C&C baseline and Reranker '10 respectively when the dependency length is between 21 and 25, though the number of these dependencies is relatively small.


Distance weighting has been used in several studies (Beeferman, Berger, & Lafferty, 1997;Brosseau-Villeneuve, Nie, & Kando, 2010;Gao, Zhou, Nie, He, & Chen, 2002) as a means of estimating the association between two words.

since our work is a special case of WTD that focuses on translating polysemous opinion words for a given opinionated sentence, we need give more weight to words that are closely related to opinion words, such as the product features.


At every model update, the word distribution in topics (i.e.

we first describe a topic model that processes documents in an on-line fashion.


The number of topics, T , is a key parameter in the model that affects the granularity of the topics.

our datasets contain "background events" in the original Twitter document stream that we don't have annotations for, and "novel events" from TDT that we do have annotations for, and that form the basis of our evaluation.


Topic-specific words were not removed, because there were not many of them among the first 500 (the most frequent words tend to be general).

the sizes obtained can be seen in table 1.


these cases are very hard for the MT systems to tackle with.


We also applied above mechanism to filter out those triggers.

the intuition behind is that the head morpheme can better represent the semantics of a Ch inese word than the combination of all its component BVs.


(2012) is that they extract all single-character verbs contained in triggers as BVs (e.g., "担" (undertake, verb) and "任" (serve as, verb) are treated as two BVs for "担任" (serve as)).

take following two sentences as samples: (E1) 我们将承担所有本公司的费用。 ( We will bear all the expenses for our company.)


MPRO(ms, hm) is defined to compute the conditional probability of a trigger when it contains a HM hm and its morphological structure is ms. MORPH(w) is used to get the morphological structure of word w .

a co mponent character in English is just the basic unit to form a word instead of a semantic unit.


Argument: the entity mentions involved in an event.

besides unknown triggers 1 and word segmentation errors (Li et al., 2012), the low quality of annotated corpora and the high ratio of pseudo trigger mentions to true ones are also blamed for the low performance of Chinese event extraction.


propagated errors from the upstream processes).

basically, the general purpose for instance filtering is to reduce the class distribution imbalance by discarding harmful or superfluous instances.


entities, relations and events) from the text.

instead of calculating the average similarity, we calculate the maximum similarity to identify whether the pseudo trigger mention should be filtered out: where n is the number of STMs for the pseudo trigger mention p and ) , ( the global discrimination comes from the probability of a pseudo trigger mention belonging to the set of true trigger mentions in the training set.


This status depends only on the given sentence and the dictionary, and is irrelevant to the position tag assigned to the character.

overall, G2 outperforms B and G1 by 2.6% and 0.3%, respectively.


(2010) adopted HMM and some rules to post-process the output of the CRF discriminative approach.

last, to fairly compare different models in a more realistic condition, Table 6 shows the results of the enhanced discriminative model (denoted by ED) and the integrated model (denoted by EI) with an external dictionary (which roughly corresponds to 65% D2 coverage rate in TABlE 4, and is specified in the next section).


On the other hand, dictionary information has been utilized in the discriminative approach in the previous works of (Low et al., 2005;.

tABLE 4 gives the results for various combinations of D1 and D2 with 0.5 , where the first row "None" denotes that no dictionary (even D1) is adopted; also, the last column "OA" gives the overall performance of various domains (except News).


For example, 18.3% of "就是" (just like) occurrences are segmented as a single word, and 81.7% of them are segmented into two single-character words "就" (just) and "是" (be) under similar context in the training set.

this paper thus proposes a new abstract aggregate candidate-feature, which indicates if the assigned tag follows the corresponding position-tag of the longest dictionary matching word.


Among those 922 OOV errors, 758 (82.2%) of them are not covered by the dictionary and 401 (52.9%) out of 758 are technical terms.

last, 1 DC and 4 DC are always "No-Ambiguity" for all above three different cases.


Since no dictionary can cover all OOV words for real applications, we would like to know how this enhanced generative model performs under different dictionary coverage rates.

unlike surface features, the distribution of the proposed abstract feature, which checks if the given candidate prefers the corresponding position-tag of the longest dictionary matching word, is almost invariant across different domains.


Note that our ED result (0.968) is a little bit different from that reported in (Low et al., 2005), which gives 0.965 F-score with a smaller dictionary that is a part of ours (see the next section).

the character context is introduced to help disambiguate them.


we propose to use a logit regression model to describe the code-switch translation probability where β j refers to the effect of the j-th item in the n-tuple x on the logit of the code-switch translation probabilities, controlling the other items of the n-tuple x.


The curves in Figure 4 show the performance of the unlabeled joint model on the development dataset using SPA/PA/AP after each iteration during training.

figure 3 illustrates the basic DP structures and operations.


The inter-rater agreement 12 between users is also computed.

a tweet is chosen if it is the candidate with the greatest salience score and its similarity to any selected tweet is below a threshold ε 8 .


For each document d ∈ D with N c words and N k keyphrases: The corresponding graphical model is shown in Figure 1b, where the observed variables (i.e., words c, keyphrases k and hyper-parameters β k and β c ) are shaded.

these methods usually suffer from the over-generalization problem.


The synonym exhibit of the eighth word sense of hang can, for example, refer to the meaning of displaying something (e.g., exhibiting a drawing) or demonstrating a skill (e.g., exhibiting a talent in acting).

we introduce a feature f idx := j that is set to the index of the target sense t j .


One explanation could be that the target MWT does not occur in the comparable corpus, or the source concept occurs in the target corpora but under a non terminology-like form, or an error during the preprocessing of the corpora induces that the terminology extraction program misses the MWT.

in the previous example, if we find two English translations for the component familial (C s2 ) such as familial and family in the target language then we obtain two context vectors: V ′ s2 1 and V ′ s2 2 (see Figure 2).


before proceeding, we briefly review related work on debates.


Most of these theories are well-founded for structured text, and structured discourse annotated corpora are available to train the models.

the part-of-speech information is also used with a word.


WordNet is used for knowing the meaning of an unknown word whereas Wikipedia is used for recognizing the named entities (which are mostly absent in the WordNet) like "NBA" in the given example.

the corresponding improvement in the multiple genre prediction using concept expansion is around 7 percentage point.


the best joint and pipe-lined scores are not significantly different from each other.


4 Our various experiments are based on different combinations of classifier models (as detailed in the previous section) and decoding strategies.

they start by computing probabilities of attachments for adjacent pairs of EDUs and greedily select the highest scoring.


In an unsupervised setting, Lauer (1995) investigated two methods of approximation: 1. counts from two-word noun compounds, and 2. co-occurrences of a pair of nouns within some fixed window.

the web span feature strongly supported "jiyuu enerugī" (free energy).


In this context, it is interesting to compare it to another sparse interpretable model based on corpus co-occurrences, such as Strudel .

each dimension acts as a mixture of latent concepts.


The most important column in these tables is column three, which reports the candidate/reference length ratio for the tuning dataset as calculated using multi-bleu.

we report BLEU scores calculated using the multi-bleu scoring tool, which uses the supplied tokenization and does no retokenization; we supplied it with references that were tokenized and truecased using the same models that were used for training and tuning.


because error mining is a cyclic process, building the complete suspicion tree is usually unnecessary.


This is because at this stage of processing, the trees built bottom up for 'which fruit', 'John' and 'have' includes two NPs with LHS category N P S (Figure 6) while the r8 rule only requires one such NP.

adjunction is handled as an optional requirement which can be satisfied by the adjunction of an empty string and subscripts indicates the nature of the requirement (S for a substitution and a for adjunction).


An example of the shallow dependency trees used for the SR task is given in Figure 1.

the surface realisation algorithm first builds a derivation tree.


As mentioned in Section 2, polarity filtering (Gardent and Kow, 2005) eliminates from the search space those sets of rules which cover the input but cannot possibly lead to a valid derivation either because a substitution node cannot be filled or because a root node fails to have a matching substitution site 4 While (Gardent and Kow, 2005) applies polarity filtering to the initial search space (the set of rules selected by all literals in the input), we apply polarity filtering to each local tree while going up the input tree.

the combinatorics induced by the lack of order in flat semantic representations is less in this task.


We use contextual information from the parse tree thus modeling left and right context which, in contrast to (Niehues and Kolss, 2009), is hierarchical.

challenges remain, particularly in modeling long-range word movement.


It is possible that better results may be possible with a stronger social emphasis than was maintained in this work (e.g.

it constructs syntactic representations of a sample of language from unlabeled corpus data unsupervisedly.


joint attention vs individual attention).

while raw correlation is greater than 50%, if we discount the sentences with unfamiliar linguistic agents, there is 100% correlation between linguistic and perceptual schemas, thereby making the linguistic argument structure concrete.


In the recent TAC-2011 guided summarization task, SWING outperformed all other participant summarization systems as measured by automated ROUGE measures.

category KL-Divergence Score (cKLD) is a differential measure that calculates the importance of a word using KL Divergence.


Up to this point, we have assigned sentence-wide CSI scores; the sentence score aggregates the CSI scores of all words in the sentence.

a word with a high LLR value implies that it co-occurs in both categories surprisingly often, or surprisingly rarely.


Most of the prior work in guided summarization focuses on producing a summary by selecting relevant aspects common within a single topic.

while not ideal, these measures have been found to generally correlate well with manual judgments (Lin and Hovy, 2003).


Such instances (67 in our study) were discarded.

we can further boost performance by selectively acquiring annotations for difficult and under-represented categories of instances.


A number of different word alignment heuristics are implemented; in the end, growdiag-final heuristic shows better performance than others on our aligned corpus.

unlike (Zhang et al., 2006), D&WA method use commonly used Mutual information (MI) and t-value to determine left and right boundary of Tibetan BaseNP in the extension step.


Compared with English or Chinese BaseNP identification work, the proposed methods doesn't get the best score, but the approach is very novel to Tibetan BaseNP identification.

the chunker is puzzled by those multi-used words.


The reference datasets are fed into the rule-based component so that the outputs represent the annotated datasets which are exploited in the feature extraction phase to generate the feature set data files in order to be utilized by the ML-based component.

mL-based NER systems take advantage of the mL algorithms in order to learn NE tagging decisions from annotated texts.


The rule-based approach relies on handcrafted local grammatical rules, while ML-based approach takes advantage of the ML algorithms that utilize sets of features extracted from datasets annotated with NEs for building NER systems.

have developed an Arabic NER system, ANERsys 1.0, which uses ME.


Moreover, when the conjunction is i 'and', it may often be omitted, without any obvious change of meaning: (40) Kto who.NOM kogo who.ACC będzie will uczył?

one aspect of coordination that has remained especially elusive in grammar engineering is the possibility to coordinate elements which are unlike in some sense.


Valence dictionaries we are aware of, Polish or otherwise, do not include such 'coordinatability' information.

there is, however, a solution which does not require extending the formal apparatus of LFG, although it is based on a relatively rarely used LFG mechanism, namely, the so-called off-path constraints (Dalrymple, 2001, p. 148).


This finding is not very surprising, but the amount of loss and the fact that it is observed consistently in many evaluation scenarios was unexpected.

the results are presented in Figure 1 (left).


Domain-adaptation is a very active research topic within the area of SMT.

for the systems tuned and tested on in-domain data, this score is as low as 1.80.


Sentences are used as nodes in the graph and the computational process can be described as: where PR(s i ) is the score of sentence s i and |S| denotes the number of sentences in a document cluster.

nowadays, some machine learning algorithms have also been applied in summarization for learning optimal feature weights automatically, for instance, some leaning to rank models (Svore et al., 2007;Jin et al., 2010;Shen and Li, 2011) have been introduced in summarization tasks.